The problem of spike encoding of sound consists in transforming a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural networks, where it is the first and most crucial stage of processing. Many algorithms have been proposed to perform spike encoding of sound. However, a systematic approach to quantitatively evaluate their performance is currently lacking. We propose the use of an information-theoretic framework to solve this problem. Specifically, we evaluate the coding efficiency of four spike encoding algorithms on two coding tasks that consist of coding the fundamental characteristics of sound: frequency and amplitude. The algorithms investigated are: Independent Spike Coding, Send-on-Delta coding, Ben's Spiker Algorithm, and Leaky Integrate-and-Fire coding. Using the tools of information theory, we estimate the information that the spikes carry on relevant aspects of an input stimulus. We find disparities in the coding efficiencies of the algorithms, where Leaky Integrate-and-Fire coding performs best. The information-theoretic analysis of their performance on these coding tasks provides insight on the encoding of richer and more complex sound stimuli.

Cette enquête porte sur le bien-être de la communauté étudiante internationale de l’Université de Sherbrooke. Elle a été réalisée durant le mois de juin 2021, durant la pandémie de la COVID-19. Elle a porté sur trois thèmes : l’adaptation au Québec, la perception de la discrimination, et les impacts de la pandémie. Il s’agit de l’une des rares enquêtes d’envergure faites sur les étudiants internationaux universitaires du Québec. L’enquête a été faite par sondage bilingue en ligne envoyé à l’ensemble des étudiants internationaux de l’UdeS inscrits à la session d’été 2021. Le sondage a recueilli 425 réponses valides, ce qui constitue un taux de réponse de 37.6%. Les résultats du sondage sont analysés et croisés en fonction du sexe, de la région de provenance, du niveau de français, et de l’année d’arrivée au Québec des répondants. Un seuil de significativité statistique de 1% est utilisé. L’analyse des résultats a permis de formuler 20 recommandations à l’Université de Sherbrooke afin d’améliorer la situation des étudiants internationaux.

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Spike encoding of sound consists in converting a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural network applications, where it is the first and a crucial stage of processing. Many spike encoding techniques exist, but there is no systematic approach to quantitatively evaluate their performance. This work proposes the use of three efficiency measures based on information theory to solve this problem. The first, coding efficiency, measures the fraction of information that the spikes encode on the amplitude of the input signal. The second, computational efficiency, measures the information encoded subject to abstract computational costs imposed on the algorithmic operations of the spike encoding technique. The third, energy efficiency, measures the actual energy expended in the implementation of a spike encoding task. These three efficiency measures are used to evaluate the performance of four spike encoding techniques for sound on the encoding of a cochleagram representation of speech data. The spike encoding techniques are: Independent Spike Coding, Send-on-Delta coding, Ben's Spiker Algorithm, and Leaky Integrate-and-Fire coding. The results show that Leaky Integrate-and-Fire coding has the overall best performance in terms of coding, computational, and energy efficiency.